{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fall / Non Fall Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Training and Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Rows, Columns) in training dataset (480, 136)\n",
      "Number of Features in training dataset:- 136\n",
      "Number of Features in testing dataset:- 136\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier,AdaBoostClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn import ensemble\n",
    "import xgboost as xgb\n",
    "from sklearn.feature_selection import VarianceThreshold, RFE\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#Random values generated should be same everytime the program is run\n",
    "np.random.seed(7)\n",
    "\n",
    "#Reading of Training and Testing Data from CSV File\n",
    "data=pd.read_csv(\"Fall15.csv\")\n",
    "train=data.iloc[0:480,:]\n",
    "test=data.iloc[480:,:]\n",
    "print(\"(Rows, Columns) in training dataset\",train.shape)\n",
    "print(\"Number of Features in training dataset:-\",train.shape[1])\n",
    "print(\"Number of Features in testing dataset:-\",test.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering\n",
    "Replace NaN values with minimun of that column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "#Transforming Columns containg \"NaN\" Values\n",
    "#in both training and testing dataset\n",
    "for col in train.columns:\n",
    "    val = train[col].min()\n",
    "    train[col]=train[col].fillna(value=val,axis=0)\n",
    "\n",
    "for col in test.columns:\n",
    "    val = test[col].min()\n",
    "    test[col]=test[col].fillna(value=val,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Train and Test Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Separating training data Features and Predictions into different dataframes\n",
    "X_train=train.drop('class_label',axis=1)\n",
    "Y_train=train['class_label']\n",
    "\n",
    "#Separating testing data Features and Predictions into different dataframes\n",
    "X_test=test.drop('class_label',axis=1)\n",
    "Y_test=test['class_label']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py:179: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
      "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5fe99c8710>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sQZ9kDfB3wKXAucBVSc4dx3tJkhY2riP684Dpqnq8qv4XuA3YMqb3kiQtYMk3Bx9iHbBv\n3voM8K75A5JsA7a11eeSPDqmXk5EZwI/X+0mhstqN6Bj7/j42cxx87P5u6MMGlfQD/qvVL+xUrUd\n2D6m9z+hJZmqqsnV7kM6kj+bq2NcUzczwIZ56+uB/WN6L0nSAsYV9N8FNiU5J8kpwJXAzjG9lyRp\nAWOZuqmqF5JcB/wrsAa4tar2jOO9NJBTYnql8mdzFaSqho+SJB23vDJWkjpn0EtS5wx6SercuM6j\n1zGU5C3MXXm8jrnrFfYDO6tq76o2JukVwSP641ySjzH3FRMBHmLu1NYAX/XL5CSBZ90c95L8J/C2\nqvq/I+qnAHuqatPqdCYdXZKrq+qLq93HicIj+uPfi8AbB9TXtm3SK9GnVruBE4lz9Me/DwP3JHmM\nl75I7neANwPXrVpXOuEl2X20TcDZx7KXE51TNx1I8irmvhp6HXP/E80A362qQ6vamE5oSZ4E3gM8\nfeQm4D+qatC/RDUGHtF3oKpeBB5Y7T6kI9wJvLaqdh25Icn9x76dE5dH9JLUOf8YK0mdM+glqXMG\nvSR1zqCXpM79P/hY7DvHtOZyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fe9a02f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.class_label.value_counts().plot(kind='bar',colors='YR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection\n",
    "### Remove Low Variance Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Features in training dataset(before feature selection by Variance Threshold):- 135\n",
      "Number of Features in training dataset(after feature selection by Variance Threshold):- 132\n"
     ]
    }
   ],
   "source": [
    "#Performing Feature Selection on Training Dataset\n",
    "print(\"Number of Features in training dataset(before feature selection by Variance Threshold):-\",X_train.shape[1])\n",
    "X_train_temp = X_train.copy(deep=True)  # Make a deep copy of the Training Data dataframe\n",
    "selector = VarianceThreshold(0.12)\n",
    "selector.fit(X_train_temp)\n",
    "X_res = X_train_temp.loc[:, selector.get_support(indices=False)]\n",
    "X_train=X_res\n",
    "print(\"Number of Features in training dataset(after feature selection by Variance Threshold):-\",X_train.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation between each feature and class variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Find most important features relative to target through correlation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py:179: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
      "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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zmTn/uZkdmXP8CHKOA46TdHDOW91ISNoSWNfMTpO0ArC0mfVao+jVntOB0yXt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fe9a02be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "traini = pd.concat([X_train, Y_train], axis=1, join='inner')\n",
    "\n",
    "# Find most important features relative to target i.e finding correlation of every individual feature i.e independent variable with dependent variable and then sorting them and using the features that have maximum correlation\n",
    "print(\"Find most important features relative to target through correlation\")\n",
    "corr = traini.corr()\n",
    "corr.sort_values([\"class_label\"], ascending = False, inplace = True)\n",
    "\n",
    "#Selecting top-20 features\n",
    "corr.class_label[1:20].plot(kind='bar',colors='RGYBC')\n",
    "top_features=corr.class_label[1:20].to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top 20 Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['mean_waistAngularVelocityZ', 'mean_waistAccelerationY', 'var_waistAccelerationX', 'var_headAccelerationY', 'var_sternumAccelerationY', 'var_waistAccelerationY', 'mean_headAccelerationY', 'mean_sternumAccelerationY', 'var_waistAngularVelocityZ', 'mean_waistAngularVelocityX', 'var_sternumAccelerationX', 'var_rthighAccelerationZ', 'mean_rthighAccelerationZ', 'var_rthighMagneticFieldX', 'var_sternumMagneticFieldX', 'var_sternumAngularVelocityZ', 'var_waistMagneticFieldX', 'mean_sternumAngularVelocityZ', 'mean_headAngularVelocityZ']\n"
     ]
    }
   ],
   "source": [
    "features=[]\n",
    "columns=top_features.index\n",
    "for col in columns:\n",
    "    features.append(col)\n",
    "print(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select same features in Training and Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n",
      "Index(['mean_waistAngularVelocityZ', 'mean_waistAccelerationY',\n",
      "       'var_waistAccelerationX', 'var_headAccelerationY',\n",
      "       'var_sternumAccelerationY', 'var_waistAccelerationY',\n",
      "       'mean_headAccelerationY', 'mean_sternumAccelerationY',\n",
      "       'var_waistAngularVelocityZ', 'mean_waistAngularVelocityX',\n",
      "       'var_sternumAccelerationX', 'var_rthighAccelerationZ',\n",
      "       'mean_rthighAccelerationZ', 'var_rthighMagneticFieldX',\n",
      "       'var_sternumMagneticFieldX', 'var_sternumAngularVelocityZ',\n",
      "       'var_waistMagneticFieldX', 'mean_sternumAngularVelocityZ',\n",
      "       'mean_headAngularVelocityZ'],\n",
      "      dtype='object')\n",
      "19\n",
      "Index(['mean_waistAngularVelocityZ', 'mean_waistAccelerationY',\n",
      "       'var_waistAccelerationX', 'var_headAccelerationY',\n",
      "       'var_sternumAccelerationY', 'var_waistAccelerationY',\n",
      "       'mean_headAccelerationY', 'mean_sternumAccelerationY',\n",
      "       'var_waistAngularVelocityZ', 'mean_waistAngularVelocityX',\n",
      "       'var_sternumAccelerationX', 'var_rthighAccelerationZ',\n",
      "       'mean_rthighAccelerationZ', 'var_rthighMagneticFieldX',\n",
      "       'var_sternumMagneticFieldX', 'var_sternumAngularVelocityZ',\n",
      "       'var_waistMagneticFieldX', 'mean_sternumAngularVelocityZ',\n",
      "       'mean_headAngularVelocityZ'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Selecting Features for training dataset\n",
    "X_train=X_train[features]\n",
    "print(X_train.shape[1])\n",
    "print(X_train.columns)\n",
    "\n",
    "#Selecting Same Features for testing dataset\n",
    "X_test=X_test[X_train.columns]\n",
    "print(X_test.shape[1])\n",
    "print(X_test.columns)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Models and Comparing them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Creating training and cross-validation dataset\n",
    "X_train,X_CV,Y_train,Y_CV=train_test_split(X_train,Y_train,test_size=0.30)\n",
    "\n",
    "\n",
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    #plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    \n",
    "results_sensitivity={}\n",
    "results_specitivity={}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 100.0 Specificity 94.8717948718\n",
      "Error Rate:\n",
      "False Positive Rate- 5.12820512821 True Positive Rate- 0.0\n",
      "Confusion matrix, without normalization\n",
      "[[111   6]\n",
      " [  0  27]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.94871795  0.05128205]\n",
      " [ 0.          1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fc350d6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KiN+tsI3vr8U2bgYOjYgXJY0ABhcsW3FdkW/7lIgoDC8k9azldq0G3n2zujQe\n+LKkPgCS1pW0BTAJ6CWpd97um6t5/eNkXy9ePX7TDviIrBdUbQxwTMFYVff8SxT+BXxNUmtJbcl2\nFdekLTBb0jrAsBWWHS6pWV7zZsDkfNsn5u2RtIWk9YrYjtWCe0pWZyJiTt7juFNSy3z2uRHxhqSR\nwIOS5gJjgW1WsYrTgBskHUt2l80TI+JpSePyQ+4P5+NKWwFP5z21j4FvRcQESXcDE4EZZLuYa/JT\n4D95+5dZPvwmA0+S3b/qhIj4XNLvycaaJuQ315sDHFrcT8eK5WvfzCwp3n0zs6Q4lMwsKQ4lM0uK\nQ8nMkuJQMrOkOJTMLCkOJTNLikPJzJLy/wER0r/KLNsAnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fbfbaba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "# Applying Logistic Regression\n",
    "log_reg=LogisticRegression()\n",
    "log_reg=log_reg.fit(X_train, Y_train)\n",
    "log_pred=log_reg.predict(X_CV)\n",
    "score=accuracy_score(Y_CV,log_pred)\n",
    "#print(score)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, log_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['Logistic']=sensitivity\n",
    "results_specitivity['Logistic']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,log_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 96.2962962963 Specificity 98.2905982906\n",
      "Error Rate:\n",
      "False Positive Rate- 1.7094017094 True Positive Rate- 3.7037037037\n",
      "Confusion matrix, without normalization\n",
      "[[115   2]\n",
      " [  1  26]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.98290598  0.01709402]\n",
      " [ 0.03703704  0.96296296]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fbfbada58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uPOvu+8Xb+ww4PWFZG+AQYBDw13gfTgfWuvt+8frPMLO2SWxHKkBXdEuy6prZ\n9Pj9W8ADRA+cW+ju78XzDwD2Ad6Jv2ylFvAu0AWY7+5zAMzs78CZ29jGYcDJAO5eDKyN7/FLdET8\nmhZP1ycKqd2B59z923gbLyaxT93N7FqiQ8T6wMSEZU/FV8zPMbN58T4cAfRIGG/Ki7c9O4ltSZIU\nSpKsDe7eK3FGHDzrE2cBr7j7L8u06wVU1lW6Btzg7veU2cbvdmIbDwFD3P0jMxsBDExYVnZdHm97\npLsnhhdm1qaC25Vy6PBNKtN7wIFm1gHAzHYzs07ALKCtmbWP2/1yOz//GtHXi5eM3+wBfEPUCyox\nETgtYawqP/4ShTeBn5pZXTPbnehQcUd2B5aaWU1gWJllQ80sJ665HfB5vO2z4/aYWSczq5fEdqQC\n1FOSSuPuK+Iex+NmVjuefYW7zzazM4EJZrYSeBvovo1VjALuNbPTiZ6yeba7v2tm78Sn3P8Vjyt1\nBd6Ne2rrgJPcfaqZPQlMBxYSHWLuyJXA+3H7jykdfp8DbxA9v+osd//OzO4nGmuaGj9cbwUwJLm/\nHUmW7n0TkaDo8E1EgqJQEpGgKJREJCgKJREJikJJRIKiUBKRoCiURCQoCiURCcr/AyQW0WMo3Ms0\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fb899f208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Decision Tree\n",
    "decision_reg = DecisionTreeClassifier(criterion='gini',\n",
    "                                            max_depth=10, max_leaf_nodes=23, splitter='best')\n",
    "decision_reg=decision_reg.fit(X_train, Y_train)\n",
    "decision_pred=decision_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, decision_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['DT']=sensitivity\n",
    "results_specitivity['DT']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,decision_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 96.2962962963 Specificity 88.8888888889\n",
      "Error Rate:\n",
      "False Positive Rate- 11.1111111111 True Positive Rate- 3.7037037037\n",
      "Confusion matrix, without normalization\n",
      "[[104  13]\n",
      " [  1  26]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.88888889  0.11111111]\n",
      " [ 0.03703704  0.96296296]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fbfc6f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fbfc5d7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Gaussian Naive Bayes\n",
    "naivebay_reg = GaussianNB()\n",
    "naivebay_reg=naivebay_reg.fit(X_train, Y_train)\n",
    "naivebay_pred=naivebay_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, naivebay_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['NB']=sensitivity\n",
    "results_specitivity['NB']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,naivebay_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 0.0 Specificity 100.0\n",
      "Error Rate:\n",
      "False Positive Rate- 0.0 True Positive Rate- 100.0\n",
      "Confusion matrix, without normalization\n",
      "[[117   0]\n",
      " [ 27   0]]\n",
      "Normalized confusion matrix\n",
      "[[ 1.  0.]\n",
      " [ 1.  0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fb88cf978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hwCOSFgKTgH02sohLgbslnUd2l82LIuJpSU/mh9z/no8r7Q08nffUPgTOjogX\nJI0EXgLmkO1ibs7/AZ7J209l3fB7HfgH2f2rLoyIjyX9hmys6YX85noLgFPr9tOxuvK1b2aWFO++\nmVlSHEpmlhSHkpklxaFkZklxKJlZUhxKZpYUh5KZJcWhZGZJ+f83EoQDBY9NJAAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fbfbd1d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Support Vector Machines\n",
    "svm_reg = SVC()\n",
    "svm_reg=svm_reg.fit(X_train, Y_train)\n",
    "svm_pred=svm_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, svm_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "cm=confusion_matrix(Y_CV,svm_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adaboost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 96.2962962963 Specificity 99.1452991453\n",
      "Error Rate\n",
      "False Positive Rate- 0.854700854701 True Positive Rate- 3.7037037037\n",
      "Confusion matrix, without normalization\n",
      "[[116   1]\n",
      " [  1  26]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.99145299  0.00854701]\n",
      " [ 0.03703704  0.96296296]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fb88f0ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fb88b7b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying AdaBoost Classifier\n",
    "ada_reg = AdaBoostClassifier(random_state=2, n_estimators=500)\n",
    "ada_reg=ada_reg.fit(X_train, Y_train)\n",
    "ada_pred=ada_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, ada_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['Adaboost']=sensitivity\n",
    "results_specitivity['Adaboost']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,ada_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 96.2962962963 Specificity 95.7264957265\n",
      "Error Rate:\n",
      "False Positive Rate- 4.2735042735 True Positive Rate- 3.7037037037\n",
      "Confusion matrix, without normalization\n",
      "[[112   5]\n",
      " [  1  26]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.95726496  0.04273504]\n",
      " [ 0.03703704  0.96296296]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fb881fb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mM+L3/wEeJnrg3EJ3nxLP7wHsC7wbf9lKNWAy0A6Y7+7zAMzsCeDcrWzjCOBM\nAHcvBNbE9/glOjp+fRhP1yIKqd2AMe7+Q7yNl5PYp45m9meiQ8RawMSEZc/GV8zPM7Mv4304Gtg/\nYbypdrztuUlsS5KkUJJkrXf3zokz4uBZlzgLeN3df1uiXWegvK7SNeAmd7+/xDYG78A2HgX6uvtM\nMxsA9ExYVnJdHm97kLsnhhdm1qKM25VS6PBNytMU4GAzawNgZrua2d7AbKClmbWO2/12Gz//JtHX\nixeN3+wOfE/UCyoyETg7YayqafwlCv8GTjSzGma2G9Gh4vbsBiwzs6rA6SWW9TOznLjmVsCceNvn\nx+0xs73NrGYS25EyUE9Jyo27r4h7HE+Z2S7x7Kvdfa6ZnQtMMLOVwDtAx62s4hLgATMbSPSUzfPd\nfbKZvRufcn81HldqD0yOe2prgTPcfbqZPQPMABYSHWJuzx+BqXH7jykefnOAt4meX3Weu/9oZg8R\njTVNjx+utwLom9x/HUmW7n0TkaDo8E1EgqJQEpGgKJREJCgKJREJikJJRIKiUBKRoCiURCQoCiUR\nCcr/A32PzbpjNvQ/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fb87e0f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Gradient Boosting Classifier\n",
    "params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 2, 'learning_rate': 0.01}\n",
    "gb_reg = ensemble.GradientBoostingClassifier(**params)\n",
    "gb_reg=gb_reg.fit(X_train, Y_train)\n",
    "gb_pred=gb_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, gb_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['GBT']=sensitivity\n",
    "results_specitivity['GBT']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,gb_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 96.2962962963 Specificity 96.5811965812\n",
      "Error Rate:\n",
      "False Positive Rate- 3.4188034188 True Positive Rate- 3.7037037037\n",
      "Confusion matrix, without normalization\n",
      "[[113   4]\n",
      " [  1  26]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.96581197  0.03418803]\n",
      " [ 0.03703704  0.96296296]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fb8795cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fb87ff2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Random Forest Classifier\n",
    "rf_reg = RandomForestClassifier(random_state=1)\n",
    "rf_reg=rf_reg.fit(X_train, Y_train)\n",
    "rf_pred=rf_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, rf_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['RForest']=sensitivity\n",
    "results_specitivity['RForest']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,rf_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Majority Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 96.2962962963 Specificity 95.7264957265\n",
      "Error Rate:\n",
      "False Positive Rate- 4.2735042735 True Positive Rate- 3.7037037037\n",
      "Confusion matrix, without normalization\n",
      "[[112   5]\n",
      " [  1  26]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.95726496  0.04273504]\n",
      " [ 0.03703704  0.96296296]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fb8768e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mM+L3/wEeJnrg3EJ3nxLP7wHsC7wbf9lKNWAy0A6Y7+7zAMzsCeDcrWzjCOBM\nAHcvBNbE9/glOjp+fRhP1yIKqd2AMe7+Q7yNl5PYp45m9meiQ8RawMSEZc/GV8zPM7Mv4304Gtg/\nYbypdrztuUlsS5KkUJJkrXf3zokz4uBZlzgLeN3df1uiXWegvK7SNeAmd7+/xDYG78A2HgX6uvtM\nMxsA9ExYVnJdHm97kLsnhhdm1qKM25VS6PBNytMU4GAzawNgZrua2d7AbKClmbWO2/12Gz//JtHX\nixeN3+wOfE/UCyoyETg7YayqafwlCv8GTjSzGma2G9Gh4vbsBiwzs6rA6SWW9TOznLjmVsCceNvn\nx+0xs73NrGYS25EyUE9Jyo27r4h7HE+Z2S7x7Kvdfa6ZnQtMMLOVwDtAx62s4hLgATMbSPSUzfPd\nfbKZvRufcn81HldqD0yOe2prgTPcfbqZPQPMABYSHWJuzx+BqXH7jykefnOAt4meX3Weu/9oZg8R\njTVNjx+utwLom9x/HUmW7n0TkaDo8E1EgqJQEpGgKJREJCgKJREJikJJRIKiUBKRoCiURCQoCiUR\nCcr/A32PzbpjNvQ/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fb86e1c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Applying Majority Voting Concept\n",
    "majority_class = VotingClassifier(estimators=[('lr', ada_reg),\n",
    "                                    ('gnb', gb_reg),('naive_bayes',naivebay_reg)],\n",
    "                                    voting='hard')\n",
    "majority_class = majority_class.fit(X_train, Y_train)\n",
    "majority_pred=majority_class.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, majority_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['MVoting']=sensitivity\n",
    "results_specitivity['MVoting']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,majority_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fb7987ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(results_sensitivity)), list(results_sensitivity.values()),color='RGBYC',width=0.3)\n",
    "plt.xticks(range(len(results_sensitivity)), list(results_sensitivity.keys()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EbJ5aPl1WJfkJ4Bzg3X0tz8jAnmSSu5K8I8kX+tx5Vt8+keQv+/Y/TvKVWQZQM6uqo+If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\nuyQ1yHCXpAYZ7pLUoP8PFMMcTvExneAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fb794c470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(results_specitivity)), list(results_specitivity.values()),color='RGBYC',width=0.3)\n",
    "plt.xticks(range(len(results_specitivity)), list(results_specitivity.keys()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
